Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
A systematic literature review of breast cancer diagnosis using machine intelligence techniques
Breast cancer is one of the most common diseases in women; it can have long-term
implications and can even be fatal. However, early detection, achieved through recent …
implications and can even be fatal. However, early detection, achieved through recent …
Optimal feature selection-based medical image classification using deep learning model in internet of medical things
Internet of Medical Things (IoMT) is the collection of medical devices and related
applications which link the healthcare IT systems through online computer networks. In the …
applications which link the healthcare IT systems through online computer networks. In the …
BreastNet18: a high accuracy fine-tuned VGG16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images
Simple Summary Breast cancer diagnosis at an early stage using mammography is
important, as it assists clinical specialists in treatment planning to increase survival rates …
important, as it assists clinical specialists in treatment planning to increase survival rates …
Multi-view feature fusion based four views model for mammogram classification using convolutional neural network
Breast cancer is the second most common cause of cancer-related deaths among women.
Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast …
Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast …
Automated breast mass classification system using deep learning and ensemble learning in digital mammogram
SJ Malebary, A Hashmi - IEEE Access, 2021 - ieeexplore.ieee.org
In recent years, deep learning techniques are employed in the mammography processing
field to reduce radiologists' costs. Existing breast mass classification systems are …
field to reduce radiologists' costs. Existing breast mass classification systems are …
Artificial neural network based breast cancer screening: a comprehensive review
Breast cancer is a common fatal disease for women. Early diagnosis and detection is
necessary in order to improve the prognosis of breast cancer affected people. For predicting …
necessary in order to improve the prognosis of breast cancer affected people. For predicting …
Multi-view convolutional neural networks for mammographic image classification
L Sun, J Wang, Z Hu, Y Xu, Z Cui - IEEE Access, 2019 - ieeexplore.ieee.org
In recent years, deep learning has been widely applied for mammographic image
classification. However, most of the existing methods are based on a single mammography …
classification. However, most of the existing methods are based on a single mammography …
Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features
Objective. Handcrafted radiomics features or deep learning model-generated automated
features are commonly used to develop computer-aided diagnosis schemes of medical …
features are commonly used to develop computer-aided diagnosis schemes of medical …
Are you paying attention? Detecting distracted driving in real-time
Each year, millions of people lose their lives to fatal road accidents. An ever increasing
proportion of these accidents is due to distracted driving caused by co-passengers and …
proportion of these accidents is due to distracted driving caused by co-passengers and …